Reducing Costs of Breast Examination: Ultrasound Performance and Inter-Observer Variability of Expert Radiologists Versus Residents

Intraoperative evaluation of surgical margins in breast cancer

Achieving clear resection margins at the time of lumpectomy is essential for optimal patient outcomes. Margin status is traditionally determined by pathologic evaluation of the specimen and often is difficult or impossible for the surgeon to definitively know at the time of surgery, resulting in the need for re-operation to obtain clear surgical margins. Numerous techniques have been investigated to enhance the accuracy of intraoperative margin and are reviewed in this manuscript.

Deep learning combined with attention mechanisms to assist radiologists in enhancing breast cancer diagnosis: a study on photoacoustic imaging

Accurate prediction of breast cancer (BC) is essential for effective treatment planning and improving patient outcomes. This study proposes a novel deep learning (DL) approach using photoacoustic (PA) imaging to enhance BC prediction accuracy. We enrolled 334 patients with breast lesions from Shenzhen People's Hospital between January 2022 and January 2024. Our method employs a ResNet50-based model combined with attention mechanisms to analyze photoacoustic ultrasound (PA-US) images. Experiments demonstrated that the PAUS-ResAM50 model achieved superior performance, with an AUC of 0.917 (95% CI: 0.884 -0.951), sensitivity of 0.750, accuracy of 0.854, and specificity of 0.920 in the training set. In the testing set, the model maintained high performance with an AUC of 0.870 (95% CI: 0.778-0.962), sensitivity of 0.786, specificity of 0.872, and accuracy of 0.836. Our model significantly outperformed other models, including PAUS-ResNet50, BMUS-ResAM50, and BMUS-ResNet50, as validated by the DeLong test (p < 0.05 for all comparisons). Additionally, the PAUS-ResAM50 model improved radiologists' diagnostic specificity without reducing sensitivity, highlighting its potential for clinical application. In conclusion, the PAUS-ResAM50 model demonstrates substantial promise for optimizing BC diagnosis and aiding radiologists in early detection of BC.

Boundary-Guided and Region-Aware Network With Global Scale-Adaptive for Accurate Segmentation of Breast Tumors in Ultrasound Images

Breast ultrasound (BUS) image segmentation is a critical procedure in the diagnosis and quantitative analysis of breast cancer. Most existing methods for BUS image segmentation do not effectively utilize the prior information extracted from the images. In addition, breast tumors have very blurred boundaries, various sizes and irregular shapes, and the images have a lot of noise. Thus, tumor segmentation remains a challenge. In this article, we propose a BUS image segmentation method using a boundary-guided and region-aware network with global scale-adaptive (BGRA-GSA). Specifically, we first design a global scale-adaptive module (GSAM) to extract features of tumors of different sizes from multiple perspectives. GSAM encodes the features at the top of the network in both channel and spatial dimensions, which can effectively extract multi-scale context and provide global prior information. Moreover, we develop a boundary-guided module (BGM) for fully mining boundary information. BGM guides the decoder to learn the boundary context by explicitly enhancing the extracted boundary features. Simultaneously, we design a region-aware module (RAM) for realizing the cross-fusion of diverse layers of breast tumor diversity features, which can facilitate the network to improve the learning ability of contextual features of tumor regions. These modules enable our BGRA-GSA to capture and integrate rich global multi-scale context, multi-level fine-grained details, and semantic information to facilitate accurate breast tumor segmentation. Finally, the experimental results on three publicly available datasets show that our model achieves highly effective segmentation of breast tumors even with blurred boundaries, various sizes and shapes, and low contrast.

Peritoneal Carcinosis: What the Radiologist Needs to Know

Peritoneal carcinosis is a condition characterized by the spread of cancer cells to the peritoneum, which is the thin membrane that lines the abdominal cavity. It is a serious condition that can result from many different types of cancer, including ovarian, colon, stomach, pancreatic, and appendix cancer. The diagnosis and quantification of lesions in peritoneal carcinosis are critical in the management of patients with the condition, and imaging plays a central role in this process. Radiologists play a vital role in the multidisciplinary management of patients with peritoneal carcinosis. They need to have a thorough understanding of the pathophysiology of the condition, the underlying neoplasms, and the typical imaging findings. In addition, they need to be aware of the differential diagnoses and the advantages and disadvantages of the various imaging methods available. Imaging plays a central role in the diagnosis and quantification of lesions, and radiologists play a critical role in this process. Ultrasound, computed tomography, magnetic resonance, and PET/CT scans are used to diagnose peritoneal carcinosis. Each imaging procedure has advantages and disadvantages, and particular imaging techniques are recommended based on patient conditions. Our aim is to provide knowledge to radiologists regarding appropriate techniques, imaging findings, differential diagnoses, and treatment options. With the advent of AI in oncology, the future of precision medicine appears promising, and the interconnection between structured reporting and AI is likely to improve diagnostic accuracy and treatment outcomes for patients with peritoneal carcinosis.

Diagnostic accuracy of radiography, digital breast tomosynthesis, micro-CT and ultrasound for margin assessment during breast surgery: A systematic review and meta-analysis

RATIONALE AND OBJECTIVES

Achieving adequate resection margins in breast conserving surgery is challenging and often demands more than one surgical procedure. We evaluated pooled diagnostic sensitivity, and specificity of radiological methods for intraoperative margin assessment and their impact on repeat surgery rate.

MATERIALS AND METHODS

We included studies using radiography, digital breast tomosynthesis (DBT), micro-CT, and ultrasound for intraoperative margin assessment with the histological assessment as the reference method. A systematic search was performed in PubMed, Embase, Cochrane Library, Scopus, and Web of Science. Two investigators screened the studies for eligibility criteria and extracted data of the included studies independently. The quality assessment on diagnostic accuracy studies (QUADAS)-2 tool was used. A bivariate random effect model was used to obtained pooled sensitivity and specificity of the index tests in the meta-analysis.

RESULTS

The systematic search resulted in screening of 798 unique records. Twenty-two articles with 29 radiological imaging methods were selected for meta-analysis. Pooled sensitivity and specificity and area under the curve were calculated for each of the 4 subgroups in the meta-analysis respectively: Radiography; 52%, 77%, 60%, DBT; 67%, 76%, 76%, micro-CT; 68%, 69%, 72%, and ultrasound; 72%, 78%, 80%. The repeat surgery rate was poorly reported in the included studies.

CONCLUSION

Ultrasound showed the highest and radiography the lowest diagnostic performance for intraoperative margin assessment. However, the heterogeneity between studies was high and the subgroups small. The radiological methods for margin assessment need further improvement to provide reliable guidance in the clinical workflow and to prevent repeat surgeries.

Supine versus Prone 3D Abus Accuracy in Breast Tumor Size Evaluation

Breast-conserving surgery (BCS) with negative resection margins decreases the locoregional recurrence rate. Breast cancer size is one of the main determinants of Tumor-Node-Metastasis (TNM) staging. Our study aimed to investigate the accuracy of supine 3D automated breast ultrasound (3D ABUS) compared to prone 3D ABUS in the evaluation of tumor size in breast cancer patient candidates for BCS. In this prospective two-center study (Groups 1 and 2), we enrolled patients with percutaneous biopsy-proven early-stage breast cancer, in the period between June 2019 and May 2020. Patients underwent hand-held ultrasound (HHUS), contrast-enhanced magnetic resonance imaging (CE-MRI) and 3D ABUS—supine 3D ABUS in Group 1 and prone 3D ABUS in Group 2. Histopathological examination (HE) was considered the reference standard. Bland–Altman analysis and plots were used. Eighty-eight patients were enrolled. Compared to prone, supine 3D ABUS showed better agreement with HE, with a slight tendency toward underestimation (mean difference of −2 mm). Supine 3D ABUS appears to be a useful tool and more accurate than HHUS in the staging of breast cancer.

Radiomics as a New Frontier of Imaging for Cancer Prognosis: A Narrative Review

The evaluation of the efficacy of different therapies is of paramount importance for the patients and the clinicians in oncology, and it is usually possible by performing imaging investigations that are interpreted, taking in consideration different response evaluation criteria. In the last decade, texture analysis (TA) has been developed in order to help the radiologist to quantify and identify parameters related to tumor heterogeneity, which cannot be appreciated by the naked eye, that can be correlated with different endpoints, including cancer prognosis. The aim of this work is to analyze the impact of texture in the prediction of response and in prognosis stratification in oncology, taking into consideration different pathologies (lung cancer, breast cancer, gastric cancer, hepatic cancer, rectal cancer). Key references were derived from a PubMed query. Hand searching and clinicaltrials.gov were also used. This paper contains a narrative report and a critical discussion of radiomics approaches related to cancer prognosis in different fields of diseases.

Usefulness of High-Frequency Ultrasonography in the Diagnosis of Melanoma: Mini Review

High-frequency equipment is characterized by ultrasound probes with frequencies of over 10 MHz. At higher frequencies, the wavelength decreases, which determines a lower penetration of the ultrasound beam so as to offer a better evaluation of the surface structures. This explains the growing interest in ultrasound in dermatology. This review examines the state of the art of high-frequency ultrasound (HFUS) in the assessment of skin cancer to ensure the high clinical approach and provide the best standard of evidence on which to base clinical and policy decisions.

An attention-supervised full-resolution residual network for the segmentation of breast ultrasound images

PURPOSE

Breast cancer is the most common cancer among women worldwide. Medical ultrasound imaging is one of the widely applied breast imaging methods for breast tumors. Automatic breast ultrasound (BUS) image segmentation can measure the size of tumors objectively. However, various ultrasound artifacts hinder segmentation. We proposed an attention-supervised full-resolution residual network (ASFRRN) to segment tumors from BUS images.

METHODS

In the proposed method, Global Attention Upsample (GAU) and deep supervision were introduced into a full-resolution residual network (FRRN), where GAU learns to merge features at different levels with attention for deep supervision. Two datasets were employed for evaluation. One (Dataset A) consisted of 163 BUS images with tumors (53 malignant and 110 benign) from UDIAT Centre Diagnostic, and the other (Dataset B) included 980 BUS images with tumors (595 malignant and 385 benign) from the Sun Yat-sen University Cancer Center. The tumors from both datasets were manually segmented by medical doctors. For evaluation, the Dice coefficient (Dice), Jaccard similarity coefficient (JSC), and F1 score were calculated.

RESULTS

For Dataset A, the proposed method achieved higher Dice (84.3  ±  10.0%), JSC (75.2  ±  10.7%), and F1 score (84.3  ±  10.0%) than the previous best method: FRRN. For Dataset B, the proposed method also achieved higher Dice (90.7  ±  13.0%), JSC (83.7  ±  14.8%), and F1 score (90.7  ±  13.0%) than the previous best methods: DeepLabv3 and dual attention network (DANet). For Dataset A + B, the proposed method achieved higher Dice (90.5  ±  13.1%), JSC (83.3  ±  14.8%), and F1 score (90.5  ±  13.1%) than the previous best method: DeepLabv3. Additionally, the parameter number of ASFRRN was only 10.6 M, which is less than those of DANet (71.4 M) and DeepLabv3 (41.3 M).

CONCLUSIONS

We proposed ASFRRN, which combined with FRRN, attention mechanism, and deep supervision to segment tumors from BUS images. It achieved high segmentation accuracy with a reduced parameter number.

Hereditary breast cancer: screening and risk reducing surgery

BACKGROUND

The screening modalities for women at high risk for breast cancer has received an increasing role during the last years. The aim of this study was to evaluate the performance of our screening program comparing the diagnostic sensitivity of clinical breast examination, mammography, ultrasonography (US) and magnetic resonance imaging (MRI).

METHODS

Clinical Breast examination, mammography, US and MRI for each patient with BRCA1 and BRCA2 mutation who underwent breast surgery in our Institution from October 2008 to April 2016 were retrospectively evaluated. The diagnostic accuracy for MRI and for the other surveillance tests in identifying early breast cancer were assessed.

RESULTS

Twenty-six female patients with genetic mutation underwent breast surgery. Twenty-two out of 26 (85%) developed cancer during the dedicated screening protocol whereas 4 women who underwent surgery did not have cancer. Imaging was able to detect cancer in all 22 patients (per patient sensibility of 100%), identifying all 35 neoplastic lesions (per lesion sensibility of 100%). The combination of Clinical Breast Examination, US and mammography aided the cancer diagnosis in 14 (64%) of patients with a sensitivity of 64% and specificity of 100%. MRI identified all the cancers, with sensibility and specificity of 100%. Moreover, in 8 (36%) of the 22 patients who developed breast cancers, the cancers were detected only by MRI, revealing a significant superiority respect to the other surveillance modalities (P<0.05).

CONCLUSIONS

MRI demonstrated to be the best imaging modality in detection of breast cancer even for lesion <1 cm. Prophylactic mastectomy is the most effective risk reduction strategy in women at high risk, contributing to the reduction of anxiety related to the condition of a carrier.